An autonomous vehicle (AV) is a vehicle that is capable of sensing its environment and navigating with little or no user input. It does so by employing sensing devices such as radar, lidar, image sensors, and the like. Autonomous vehicles further use information from global positioning systems (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and/or drive-by-wire systems to navigate the vehicle.
While recent years have seen significant advancements in AVs, such systems might still be improved in a number of respects. For example, it would be advantageous for an AV to be capable of determining whether the brake lights, turn signals, hazard lights and/or other exterior lamps of another vehicle in the environment are illuminated. This information would assist the AV in predicting the likely behavior of other vehicles. While machine learning models might be considered for this task, training such a model would be time-consuming—requiring significant human intervention in the form of acquiring a large number of training images (e.g., of other vehicles) and labeling those images with the appropriate “lighting state” (e.g., “brake lights on,” “left turn signal on,” etc.).
Accordingly, it is desirable to provide systems and methods that are capable of training, without the aforementioned human intervention, an AV to recognize the exterior lighting state of other vehicles in the environment. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.